Visualising CT images in 3D with ipyvolume

!rm -rf /Users/maartenbreddels/anaconda3/envs/pygrunn/lib/python3.6/site-packages/dicom*

!pip install dicom

Collecting dicom
  Using cached dicom-0.9.9.post1-py3-none-any.whl
Installing collected packages: dicom
Successfully installed dicom-0.9.9.post1

import os
import sys
import glob
import dicom
import pylab as p
import numpy as np
import ipyvolume as ipv

/Users/maartenbreddels/anaconda3/envs/pygrunn/lib/python3.6/site-packages/dicom/__init__.py:53: UserWarning: 
This code is using an older version of pydicom, which is no longer 
maintained as of Jan 2017.  You can access the new pydicom features and API 
by installing `pydicom` from PyPI.
See 'Transitioning to pydicom 1.x' section at pydicom.readthedocs.org 
for more information.

  warnings.warn(msg)

Read the individual slices

#!cp -r /data/users/jovan/kaggle/DSB2017/data/stage1/0015ceb851d7251b8f399e39779d1e7d .

# Set up the location of the individual .dcm files
datalist = glob.glob('0015ceb851d7251b8f399e39779d1e7d/*dcm')

# Read each slice in turn
slices = np.zeros(len(datalist), dtype='object')
for i,v in enumerate(datalist):
    slices[i] = dicom.read_file(v)

Display one slice as an example

datalist

['0015ceb851d7251b8f399e39779d1e7d/00ac2629fe659e68f4879da4812af659.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/0142676d603719d164fe77097c086741.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/018a0959730e9889f55670e7687af559.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/01c1ce911922eb684a0b595d4e7f7a42.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/0305df4fa1defc17ff0382a9edddb99b.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/04f4264b1ee25de0e893f72c165bf0c9.dcm',
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 '0015ceb851d7251b8f399e39779d1e7d/db899c78f6bc1392634601fbac9712a6.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/dcfb1bbf18b62e4d2f3562c51335c686.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/dd440851154e441a27348301386f3066.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/debebb44565a6ff95846a12cc62c19ff.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/e0c19874c80b65c780328f4df7eebf25.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/e33d6210cc72b6fbe2085e96a596ee68.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/e3d56ccab0f19df030a3860aff432e54.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/e3dcfdf2b41deaa04fd556d1bec0da67.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/e534560a0d1da6886020b485ab33c68d.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/e8c1b074719faa3fd9282a44905479b8.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/e961e58ac4a5a622c7370cc63f784ce2.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/e98346205672b12fe690675f5dc0ccb5.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/ea96b30f3029ff57ffba7ad575647220.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/eb6a86dba2fd609a10504ec2ca49ea68.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/ec7057b897896beed2c119934512444c.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/eeb9478cd8b1562b197fca4c0af66ac2.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f09c600a7b99ff576091cb52fe79e48a.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f304b8329f747cb308dd20399c980472.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f42d746f5e28b7b5545e2539fcf05534.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f651325dbb415b58b772908a47cfe87f.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f659585cd968d4e8371151d14f813412.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f70687ec24da1512b3f4c39a0968212b.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f7c820e8aa564b004f04fe86d7197289.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f8fa12b0c04b122c87dc76ac1469a1e5.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f9957d30978dd9756c3d3f8604136235.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/f9fcf852e65979fe497941080813b317.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/fa2d7b39de8435f1312e31b70720d0e3.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/faae519c52552083ee791955034fc0e6.dcm',
 '0015ceb851d7251b8f399e39779d1e7d/fc5ef525705ddc92373d732967adfea4.dcm']

p.figure(figsize=(9,9))
p.imshow(slices[100].pixel_array, origin='lower')
p.colorbar(fraction=0.046, pad=0.04, label='pixel values')
p.axis('off')
p.show()

Create a 3D datacube from the individual slices

instances = [i.InstanceNumber for i in slices]
idx = np.argsort(instances)
ZZ  = np.array([slices[index].pixel_array for index in idx])
d = ZZ[::2,::4,::4].T

ipv.quickvolshow(d, lighting=False)